An architecture for COVID-19 analysis and detection using big data, AI, and data architectures

Author:

Alghamdi Ahmed MohammedORCID,Al Shehri Waleed A.ORCID,Almalki JameelORCID,Jannah Najlaa,Alsubaei Faisal S.

Abstract

The COVID-19 epidemic is affecting individuals in many ways and continues to spread all over the world. Vaccines and traditional medical techniques are still being researched. In diagnosis and therapy, biological and digital technology is used to overcome the fear of this disease. Despite recovery in many patients, COVID-19 does not have a definite cure or a vaccine that provides permanent protection for a large number of people. Current methods focus on prevention, monitoring, and management of the spread of the disease. As a result, new technologies for combating COVID-19 are being developed. Though unreliable due to a lack of sufficient COVID-19 datasets, inconsistencies in the datasets availability, non-aggregation of the database because of conflicting data formats, incomplete information, and distortion, they are a step in the right direction. Furthermore, the privacy and confidentiality of people’s medical data are only partially ensured. As a result, this research study proposes a novel, cooperative approach that combines big data analytics with relevant Artificial Intelligence (AI) techniques and blockchain to create a system for analyzing and detecting COVID-19 instances. Based on these technologies, the reliability, affordability, and prominence of dealing with the above problems required time. The architecture of the proposed model will analyze different data sources for preliminary diagnosis, detect the affected area, and localize the abnormalities. Furthermore, the blockchain approach supports the decentralization of the central repository so that it is accessible to every stakeholder. The model proposed in this study describes the four-layered architecture. The purpose of the proposed architecture is to utilize the latest technologies to provide a reliable solution during the pandemic; the proposed architecture was sufficient to cover all the current issues, including data security. The layers are unique and individually responsible for handling steps required for data acquisition, storage, analysis, and reporting using blockchain principles in a decentralized P2P network. A systematic review of the technologies to use in the pandemic covers all possible solutions that can cover the issue best and provide a secure solution to the pandemic.

Funder

University of Jeddah

Publisher

Public Library of Science (PLoS)

Reference61 articles.

1. “Coronavirus.” https://www.who.int/health-topics/coronavirus#tab=tab_1.

2. Blockchain-Federated-Learning and Deep Learning Models for COVID-19 detection using CT Imaging;R. Kumar;IEEE Sens. J.,2021

3. AI Techniques for COVID-19;A. A. Hussain;IEEE Access,2020

4. Applications of big data analytics to control covid‐19 pandemic;S. J. Alsunaidi;Sensors,2021

5. Technology against COVID-19 A Blockchain-based framework for Data Quality;I. Ezzine;2020 6th IEEE Congress on Information Science and Technology (CiSt),,2020

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